People apply different grasps to objects in daily life. Those grasps, usually stable grasps that vary from power grasps to precision grasps, are executed for different manipulation purposes. In robotics, grasping problems have been studied for decades. These problems involve finding a grasp of good quality for a robotic hand to execute given an object and a manipulation task. A good quality grasp results from an appropriate placement of contacts on an object. An appropriate contact placement needs to be reachable by suitable wrist location and orientation, and requires proper hand configuration. The solutions to grasp problems can be divided into two areas: grasp planning and learning from demonstration.
Grasp planning uses optimization mathematics to search for the optimal contact placement on an object. The cost function of the optimization is a function of grasp quality measures. Grasp quality usually measures a force-closure property, which measures the capability of a grasp to apply appropriate forces on the object to resist disturbances in any direction and to equilibrate any external wrench. Such classic grasp criteria have been widely used in grasp optimization. However, they are task independent. In many manipulation tasks, such as drinking, writing, and handling a screwdriver, a grasp must be applied in a specific way or on some particular part of the object body for different purposes. Although higher grasp quality measures theoretically indicate better grasp, human grasps result in lower quality measures than grasp planning even though human grasps have a higher success rate. Humans grasp objects in a hand-shape aligned manner with the principal axis of the object and use a low-spread pinch, which leads to lower grasp qualities but a higher success rate. It has been concluded by researchers that the existing quality criteria could not be equivalent to the real grasp quality that humans use to assist their grasps.
It is natural for a robot to learn grasp and manipulation skills from humans because humans can handle the dexterous tasks easily. Humans tend to manipulate an object in an optimal way, in terms of stability and energy conservation, by adjusting their motions and contact forces according the object shape and material hardness. The approach in which a robot learns from observing humans grasp objects is called learning from demonstration (LfD). LfD has been a powerful mechanism for a teaching robot new tasks by observing people's demonstrations without any reprogramming. With the learning results, a robot can mimic human motions by reproducing movements similar to the demonstration. The LfD technique avoids a complex mathematic model for hands and objects, and provides useful task information from the demonstrations. The way of demonstration includes guidance on the robot body and execution on the teacher body. Guidance on the robot body avoids correspondence problems but is less intuitive from the teacher's perspective, because the user would lose a first-hand feeling. It also raises difficulties in the human control of a high dimensional motion of the robotic hand with multi-fingers. In contrast, a demonstration performed by a human body is more intuitive, because it requires much less effort than is needed in controlling a robotic hand. Also, humans have good senses on their own muscles and skin.
While the LfD approach is promising, the correspondence problem has been an impediment due to the kinematic difference between a human and a robot. Mapping from a force-closure demonstrated grasp to a robotic body may result in a non-force-closure robotic grasp because of this correspondence problem.